AWS Supply Chain Features
Description of the service
AWS Supply Chain integrates data and offers demand planning, integrated contextual collaboration, and actionable insights driven by machine learning.
Important aspects of the product
Data lakes
For supply chains to comprehend, retrieve, and convert heterogeneous, incompatible data into a single data model, AWS Supply Chain creates a data lake utilizing machine learning models. Data from a variety of sources, including supply chain management and ERP systems like SAP S/4HANA, can be ingested by the data lake.
AWS Supply Chain associates data from source systems to the unified data model using machine learning (ML) and natural language processing (NLP) in order to incorporate data from changeable sources like EDI 856. Predefined yet adaptable transformation procedures are used to directly transform EDI 850 and 860 messages. Amazon S3 buckets may also store data from other systems, which generative AI will map and absorb the AWS Supply Chain Data Lake.
Insights
Using the extensive supply chain data in the data lake, AWS Supply Chain automatically produces insights into possible supply chain hazards (such overstock or stock-outs) and displays them on an inventory visualization map. The inventory visualization map shows the quantity and selection of inventory that is currently available, together with the condition of each location’s inventory (e.g., inventory that is at risk of stock out).
Additionally, AWS Supply Chain provides work order analytics to show maintenance-related materials from sourcing to delivery, as well as order status, delivery risk identification, and delivery risk mitigation measures.
In order to produce more precise vendor lead-time forecasts, AWS Supply Chain uses machine learning models that are based on technology that is comparable to that used by Amazon. Supply planners can lower the risk of stock-outs or excess inventory by using these anticipated vendor lead times to adjust static assumptions included in planning models.
By choosing the location, risk type (such as stock-out or excess stock risk), and stock threshold, inventory managers, demand planners, and supply chain leaders can also make their own insight watchlists. They can then add team members as watchers. AWS Supply Chain will provide an alert outlining the possible risk and the affected locations if a risk is identified. Work order information can be used by supply chain leaders in maintenance, procurement, and logistics to lower equipment downtime, material inventory buffers, and material expedites.
Suggested activities and cooperation
When a risk is identified, AWS Supply Chain automatically assesses, ranks, and distributes several rebalancing options to give inventory managers and planners suggested courses of action. The sustainability impact, the distance between facilities, and the proportion of risk mitigated are used to rate the recommendation options. Additionally, supply chain managers can delve deeper to examine how each choice would affect other distribution hubs around the network. Additionally, AWS Supply Chain continuously learns from your choices to generate better suggestions over time.
AWS Supply Chain has built-in contextual collaboration features to assist you in reaching an agreement with your coworkers and carrying out rebalancing activities. Information regarding the risk and suggested solutions are exchanged when teams message and chat with one another. This speeds up problem-solving by lowering mistakes and delays brought on by inadequate communication.
Demand planning
In order to help prevent waste and excessive inventory expenditures, AWS Supply Chain Demand Planning produces more accurate demand projections, adapts to market situations, and enables demand planners to work across teams. AWS Supply Chain employs machine learning (ML) to evaluate real-time data (such open orders) and historical sales data, generate forecasts, and continuously modify models to increase accuracy in order to assist eliminate the manual labor and guesswork associated with demand planning. Additionally, AWS Supply Chain Demand Planning continuously learns from user inputs and shifting demand patterns to provide prediction updates in almost real-time, enabling businesses to make proactive adjustments to supply chain operations.
Supply planning
AWS Supply Chain Supply Planning anticipates and schedules the acquisition of components, raw materials, and final products. This capability takes into account economic aspects like holding and liquidation costs and builds on nearly 30 years of Amazon experience in creating and refining AI/ML supply planning models. Demand projections produced by AWS Supply Chain Demand Planning (or any other demand planning system) are among the extensive, standardized data from the AWS Supply Chain Data Lake that are used by AWS Supply Chain Supply Planning.
Your company can better adapt to changes in demand and supply interruptions, which lowers inventory costs and improves service levels. By dynamically calculating inventory targets and taking into account demand variability, actual vendor lead times, and ordering frequency, manufacturing customers can improve in-stock and order fill rates and create supply strategies for components and completed goods at several bill of materials levels.
N-Tier Visibility
AWS Supply Chain N-Tier Visibility extends visibility beyond your company to your external trading partners by integrating with Work Order Insights or Supply Planning. By enabling you to coordinate and confirm orders with suppliers, this visibility enhances the precision of planning and execution procedures. In a few simple actions, invite, onboard, and work together with your trading partners to get order commitments and finalize supply arrangements. Partners provide commitments and confirmations, which are entered into the supply chain data lake. Subsequently, this data can be utilized to detect shortages of materials or components, alter supply plans with fresh data, and offer more insightful information.
Sustainability
Sustainability experts may access the necessary documents and datasets from their supplier network more securely and effectively using AWS Supply Chain Sustainability, which employs the same underlying technology as N-Tier Visibility. Based on a single, auditable record of the data, these capabilities assist you in providing environmental and social governance (ESG) information.
AWS Supply Chain Analytics
Amazon Quicksight powers AWS Supply Chain Analytics, a reporting and analytics tool that offers both pre-made supply chain dashboards and the ability to create custom reports and analytics. With this functionality, you may utilize the AWS Supply Chain user interface to access your data in the Data Lake. You can create bespoke reports and dashboards with the inbuilt authoring tools, or you can utilize the pre-built dashboards as is or easily alter them to suit your needs. This function provides you with a centralized, adaptable, and expandable operational analytics console.
Amazon Q In the AWS Supply Chain
By evaluating the data in your AWS Supply Chain Data Lake, offering crucial operational and financial insights, and responding to pressing supply chain inquiries, Amazon Q in AWS Supply Chain is an interactive generative artificial intelligence assistant that helps you run your supply chain more effectively. Users spend less time looking for pertinent information, get solutions more quickly, and spend less time learning, deploying, configuring, or troubleshooting AWS Supply Chain.
